Understanding and automated modeling of human reading patterns can help in improving the manual crowd-sourced annotations in a variety of Natural Language Processing (NLP) tasks as they are closely intertwined with the human subject reading intent [1]}. While there is no standard practice to quantify the quality of annotator efforts, these annotations heavily feed into the NLP supervised learning setups effecting the quality of learning models. Recognizing the reading patterns for estimating the reading effort also has broader applications in medical diagnosis of reading impairments such as dyslexia [2]}, child developmental reading disorder [3]} and attention deficit disorder [4]}.
